import random
import gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
import matplotlib.pyplot as plt
import rl_utils

class PolicyNet(nn.Module):
    def __init__(self,state_dim,hidden_dim,action_dim):
        super(PolicyNet, self).__init__()
        self.fc1 = nn.Linear(state_dim,hidden_dim)
        self.fc2 = nn.Linear(hidden_dim,action_dim)
    def forward(self,x):
        x = F.relu(self.fc1(x))
        return F.softmax(self.fc2(x),dim=1)

class QValueNet(nn.Module):
    def __init__(self,state_dim,hidden_dim,action_dim):
        super(QValueNet, self).__init__()
        self.fc1 = nn.Linear(state_dim,hidden_dim)
        self.fc2 = nn.Linear(hidden_dim,action_dim)

    def forward(self,x):
        x = F.relu(self.fc1(x))
        return self.fc2(x)

class SAC:
    '''处理离散动作得SAC算法'''
    def __init__(self,state_dim,hidden_dim,action_dim,actor_lr,critic_lr,alpha_lr,target_entropy,tau,gamma,device):
        # 策略网络
        self.actor = PolicyNet(state_dim,hidden_dim,action_dim).to(device)
        # 第一个Q网络
        self.critic_1 = QValueNet(state_dim,hidden_dim,action_dim).to(device)
        # 第二个Q网络
        self.critic_2 = QValueNet(state_dim,hidden_dim,action_dim).to(device)
        # 第一个目标Q网络
        self.target_critic_1 = QValueNet(state_dim,hidden_dim,action_dim).to(device)
        # 第二个目标Q网络
        self.target_critic_2 = QValueNet(state_dim,hidden_dim,action_dim).to(device)
        # 令目标Q网络得初始参数和Q网络一样
        self.target_critic_1.load_state_dict(self.critic_1.state_dict())
        self.target_critic_2.load_state_dict(self.critic_2.state_dict())
        # 优化器
        self.actor_optimizer = torch.optim.Adam(self.actor.parameters(),lr=actor_lr)
        self.critic_1_optimizer = torch.optim.Adam(self.critic_1.parameters(),lr=critic_lr)
        self.critic_2_optimizer = torch.optim.Adam(self.critic_2.parameters(),lr=critic_lr)
        # 使用alpha的log值，可以使训练稳定
        self.log_alpha = torch.tensor(np.log(0.01),dtype=torch.float)
        self.log_alpha.requires_grad = True
        self.log_alpha_optimizer = torch.optim.Adam([self.log_alpha],lr=alpha_lr)

        self.target_entropy = target_entropy  # 目标熵大小
        self.gamma = gamma
        self.tau = tau
        self.device = device

    def take_action(self,state):
        state = torch.tensor([state],dtype=torch.float).to(self.device)
        probs = self.actor(state)
        action_dict = torch.distributions.Categorical(probs)
        action = action_dict.sample()
        return action.item()

    # 计算目标Q值，直接用策略网络的输出概率进行期望计算
    def calc_target(self,rewards,next_states,dones):
        next_probs = self.actor(next_states)
        next_log_probs = torch.log(next_probs + 1e-8)
        entropy = -torch.sum(next_probs * next_log_probs,dim=1,keepdim=True)
        q1_value = self.target_critic_1(next_states)
        q2_value = self.target_critic_2(next_states)
        min_qvalue = torch.sum(next_probs * torch.min(q1_value,q2_value),dim=1,keepdim=True)
        next_value = min_qvalue + self.log_alpha.exp() * entropy
        td_target = rewards + self.gamma * next_value * (1 - dones)
        return td_target

    def soft_update(self,net,target_net):
        for param_target,param in zip(target_net.parameters(),net.parameters()):
            param_target.data.copy_(param_target.data * (1.0-self.tau) + param.data * self.tau)

    def update(self,transition_dict):
        states = torch.tensor(transition_dict['states'],dtype=torch.float).to(self.device)
        actions = torch.tensor(transition_dict['actions']).view(-1,1).to(self.device)
        rewards = torch.tensor(transition_dict['rewards'],dtype=torch.float).view(-1,1).to(self.device)
        next_states = torch.tensor(transition_dict['next_states'],dtype=torch.float).to(self.device)
        dones = torch.tensor(transition_dict['dones'],dtype=torch.float).view(-1,1).to(self.device)

        # 更新两个Q网络
        td_target = self.calc_target(rewards,next_states,dones)

        critic_1_q_values = self.critic_1(states).gather(1,actions)
        critic_1_loss = torch.mean(F.mse_loss(critic_1_q_values,td_target.detach()))

        critic_2_q_values = self.critic_2(states).gather(1,actions)
        critic_2_loss = torch.mean(F.mse_loss(critic_2_q_values,td_target.detach()))

        self.critic_1_optimizer.zero_grad()
        critic_1_loss.backward()
        self.critic_1_optimizer.step()
        self.critic_2_optimizer.zero_grad()
        critic_2_loss.backward()
        self.critic_2_optimizer.step()

        # 更新策略网络
        probs = self.actor(states)
        log_probs = torch.log(probs + 1e-8)
        # 直接根据概率计算熵
        entropy = -torch.sum(probs * log_probs,dim=1,keepdim=True)
        q1_value = self.critic_1(states)
        q2_value = self.critic_2(states)
        # 直接根据概率计算期望
        min_qvalue = torch.sum(probs * torch.min(q1_value,q2_value),dim=1,keepdim=True)
        actor_loss = torch.mean(-self.log_alpha.exp() * entropy - min_qvalue)
        self.actor_optimizer.zero_grad()
        actor_loss.backward()
        self.actor_optimizer.step()

        # 更新alpha值
        alpha_loss = torch.mean((entropy - self.target_entropy).detach() * self.log_alpha.exp())
        self.log_alpha_optimizer.zero_grad()
        alpha_loss.backward()
        self.log_alpha_optimizer.step()

        self.soft_update(self.critic_1,self.target_critic_1)
        self.soft_update(self.critic_2,self.target_critic_2)


if __name__ == '__main__':
    actor_lr = 1e-3
    critic_lr = 1e-2
    alpha_lr = 1e-2
    num_episodes = 200
    hidden_dim = 128
    gamma = 0.98
    tau = 0.005
    buffer_size = 10000
    minimal_size = 500
    batch_size = 64
    target_entropy = -1
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    env_name = 'CartPole-v0'
    env = gym.make(env_name)
    random.seed(0)
    np.random.seed(0)
    env.seed(0)
    torch.manual_seed(0)

    replay_buffer = rl_utils.ReplayBuffer(buffer_size)
    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.n
    agent = SAC(state_dim,hidden_dim,action_dim,actor_lr,critic_lr,alpha_lr,target_entropy,tau,gamma,device)

    return_list = rl_utils.train_off_policy_agent(env, agent, num_episodes, replay_buffer, minimal_size, batch_size)

    episodes_list = list(range(len(return_list)))
    plt.plot(episodes_list, return_list)
    plt.xlabel('Episodes')
    plt.ylabel('Returns')
    plt.title('SAC on {}'.format(env_name))
    plt.show()

    mv_return = rl_utils.moving_average(return_list, 9)
    plt.plot(episodes_list, mv_return)
    plt.xlabel('Episodes')
    plt.ylabel('Returns')
    plt.title('SAC on {}'.format(env_name))
    plt.show()


